Poster — Thur Eve — 46: Image‐Based Scoring of Radiation Injury in Lung: Analysis of Sources of Uncertainties
Bibliographic record
Abstract
We are studying the robustness and uncertainties of an automated method for quantifying radiotherapy‐induced lung injury from CT images and delineate its relationship with radiation dose at different post‐radiation (post‐RT) time points. Methods: Using multi‐resolution affine optimization technique, post‐RT diagnostic CT images were registered to planning CT images. Following the registration and patient tissue‐based CT calibration, a change in physical density at each voxel position of the planning CT was evaluated and voxels which density change is considered pathological were segmented as injury. Retrospective dose calculations using anisotropic analytical algorithm (AAA) and Monte‐Carlo (MC) were performed. The segmented injury was spatially correlated to the dose distributions to deduce a patient‐specific dose‐response relationship for radiation‐induced injury. Results: We found the probability of injury as a function of dose and post‐treatment time was patient‐specific. Due to the inaccuracy of the affine registration, the injury segmentation was manually corrected for the misalignment of normal tissue features, which gave rise to a case‐dependent uncertainty of up to 10%. Inter‐patient variability in CT calibration contributed 4% or less to the uncertainty on the probability. Finally, dose calculation from MC simulation occasionally yielded a significantly modified complication probability compared to AAA model suggesting that dose calculation accuracy is important for the investigation on dose‐response of lung injury. Conclusion: The presented method provided a quantitative approach for dose‐response analysis in normal lung tissues if the accuracy in image registration and dose calculation can be assured and will provide better options to complication‐driven treatment planning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".